Leave-one-ion-out cross-validation for assisting in developing robust QSPR models of ionic liquids

Abstract

Precisely determining thermodynamic properties such as density (ρ) and viscosity (η) at variable temperatures and pressures is necessary in the chemical process design of ionic liquids (ILs). Quantitative structure–property relationship (QSPR) models offer a quick and accurate route for obtaining the thermodynamic properties of ILs under variable environmental conditions. However, QSPR models employing the leave-one-out cross-validation (LOO-CV) method are often hindered by a “pseudo-high” robustness, weakened stability, and unbalanced data point distribution when used for determining the ρ and η of ILs. In this work, we propose a rigorous evaluation method for QSPR models, called leave-one-ion-out cross-validation (LOIO-CV), to evaluate the thermodynamic properties of ILs. By balancing the distribution of data points in ILs, two f(T,P,I)-QSPR models are developed with norm indexes (I) to predict ρ and η of ILs at variable temperatures and pressures. Our LOIO-CV method enhances the stability of QSPR models when predicting the properties of ILs with novel cations and anions, which is crucial for the data-driven design of new ILs.